The use of ranging sensors, such as radar or lidar, to provide real-time information about terrain and obstacles in front of a vehicle, such as a helicopter, is common, and much work has been done to improve the quality of the resultant three-dimensional maps. One approach is to use an evidence grid to accumulate the sensor data across both space and time. A probabilistic approach using an evidence grid was disclosed in U.S. patent application Ser. No. 12/051,801, filed Mar. 19, 2008, the contents of which are hereby incorporated by reference. However, the sensor data that is available from the on-board sensors is usually limited in its coverage by a restricted field of view, limited by obscurants such as dust, limited by shadowing, and often limited by simple lack of time on target. Consequently, the resultant three-dimensional maps have lower resolution than is desired and areas lacking in data.
Another solution is to use high-resolution a priori data of the target area that is based on sensor measurements taken previously from a different platform, e.g., a UAV. These data can have very high resolution (10 cm accuracy), and contain much information that would be beneficial if it were displayed to the pilot. However, these data cannot be used in place of the real-time sensor data for two reasons. First, the a priori data is old, and may not represent the current start of the target area. Buildings may have been erected (or removed), or vehicles that were present when the a priori data was taken may have been moved. Displaying the old, out-of-date a priori data without correcting for these temporal changes is dangerous. The second problem is that the navigation system of the helicopter has errors, so that the display of the a priori data may not correspond to the current position of the helicopter. This is particularly a problem when flying close to the ground, since the navigation error associated with the altitude is typically much larger than other navigation errors.